New York Times - Articles

Dataset statistics

Number of variables	36
Number of observations	839
Missing cells	505
Missing cells (%)	1.7%
Duplicate rows	0
Duplicate rows (%)	0.0%
Total size in memory	230.5 KiB
Average record size in memory	281.3 B

Variable types

Numeric	17
Categorical	18
DateTime	1

Alerts

`Has_Comments` has constant value "1"	Constant
`headline` has a high cardinality: 838 distinct values	High cardinality
`abstract` has a high cardinality: 837 distinct values	High cardinality
`keywords` has a high cardinality: 781 distinct values	High cardinality
`uniqueID` has a high cardinality: 839 distinct values	High cardinality
`lead_paragraph` has a high cardinality: 785 distinct values	High cardinality
`headline.main` has a high cardinality: 838 distinct values	High cardinality
`df_index` is highly correlated with `month`	High correlation
`month` is highly correlated with `df_index`	High correlation
`TEXT_LeadParagraph_POS_PNOUN` is highly correlated with `TEXT_LeadParagraph_ENT_ORG` and 1 other fields	High correlation
`TEXT_Keywords_POS_PNOUN` is highly correlated with `TEXT_Keywords_ENT_ORG` and 1 other fields	High correlation
`TEXT_LeadParagraph_ENT_ORG` is highly correlated with `TEXT_LeadParagraph_POS_PNOUN`	High correlation
`TEXT_Keywords_ENT_ORG` is highly correlated with `TEXT_Keywords_POS_PNOUN`	High correlation
`TEXT_LeadParagraph_ENT_PERSON` is highly correlated with `TEXT_LeadParagraph_POS_PNOUN`	High correlation
`TEXT_Keyrwords_ENT_PERSON` is highly correlated with `TEXT_Keywords_POS_PNOUN`	High correlation
`df_index` is highly correlated with `month`	High correlation
`month` is highly correlated with `df_index`	High correlation
`TEXT_LeadParagraph_POS_PNOUN` is highly correlated with `TEXT_LeadParagraph_ENT_ORG` and 2 other fields	High correlation
`TEXT_Keywords_POS_PNOUN` is highly correlated with `TEXT_Keywords_ENT_ORG` and 1 other fields	High correlation
`TEXT_LeadParagraph_ENT_ORG` is highly correlated with `TEXT_LeadParagraph_POS_PNOUN`	High correlation
`TEXT_Keywords_ENT_ORG` is highly correlated with `TEXT_Keywords_POS_PNOUN`	High correlation
`TEXT_LeadParagraph_ENT_GPE` is highly correlated with `TEXT_LeadParagraph_POS_PNOUN`	High correlation
`TEXT_LeadParagraph_ENT_PERSON` is highly correlated with `TEXT_LeadParagraph_POS_PNOUN`	High correlation
`TEXT_Keyrwords_ENT_PERSON` is highly correlated with `TEXT_Keywords_POS_PNOUN`	High correlation
`df_index` is highly correlated with `month`	High correlation
`month` is highly correlated with `df_index`	High correlation
`TEXT_LeadParagraph_POS_PNOUN` is highly correlated with `TEXT_LeadParagraph_ENT_PERSON`	High correlation
`TEXT_Keywords_POS_PNOUN` is highly correlated with `TEXT_Keywords_ENT_ORG` and 1 other fields	High correlation
`TEXT_Keywords_ENT_ORG` is highly correlated with `TEXT_Keywords_POS_PNOUN`	High correlation
`TEXT_LeadParagraph_ENT_PERSON` is highly correlated with `TEXT_LeadParagraph_POS_PNOUN`	High correlation
`TEXT_Keyrwords_ENT_PERSON` is highly correlated with `TEXT_Keywords_POS_PNOUN`	High correlation
`TEXT_Keywords_ENT_LOC` is highly correlated with `Has_Comments`	High correlation
`newsdesk` is highly correlated with `section` and 3 other fields	High correlation
`TEXT_LeadParagraph_ENT_LOC` is highly correlated with `Has_Comments`	High correlation
`TEXT_LeadParagraph_ENT_FAC` is highly correlated with `Has_Comments`	High correlation
`section` is highly correlated with `newsdesk` and 3 other fields	High correlation
`TEXT_Keywords_ENT_NORP` is highly correlated with `Has_Comments`	High correlation
`comment_size` is highly correlated with `Has_Comments`	High correlation
`TEXT_headline.main_POS_NOUN` is highly correlated with `Has_Comments`	High correlation
`TEXT_Keywords_ENT_FAC` is highly correlated with `Has_Comments`	High correlation
`Has_Comments` is highly correlated with `TEXT_Keywords_ENT_LOC` and 10 other fields	High correlation
`material` is highly correlated with `newsdesk` and 2 other fields	High correlation
`print_section` is highly correlated with `newsdesk` and 2 other fields	High correlation
`df_index` is highly correlated with `month`	High correlation
`newsdesk` is highly correlated with `section` and 9 other fields	High correlation
`section` is highly correlated with `newsdesk` and 8 other fields	High correlation
`material` is highly correlated with `newsdesk` and 3 other fields	High correlation
`word_count` is highly correlated with `TEXT_Keywords_POS_PNOUN` and 2 other fields	High correlation
`n_comments` is highly correlated with `comment_size`	High correlation
`print_section` is highly correlated with `newsdesk` and 3 other fields	High correlation
`print_page` is highly correlated with `newsdesk` and 2 other fields	High correlation
`month` is highly correlated with `df_index`	High correlation
`comment_size` is highly correlated with `newsdesk` and 3 other fields	High correlation
`TEXT_LeadParagraph_POS_NOUN` is highly correlated with `newsdesk` and 3 other fields	High correlation
`TEXT_LeadParagraph_POS_PNOUN` is highly correlated with `newsdesk` and 6 other fields	High correlation
`TEXT_Keywords_POS_NOUN` is highly correlated with `section` and 2 other fields	High correlation
`TEXT_Keywords_POS_PNOUN` is highly correlated with `newsdesk` and 5 other fields	High correlation
`TEXT_LeadParagraph_ENT_ORG` is highly correlated with `TEXT_LeadParagraph_POS_PNOUN`	High correlation
`TEXT_Keywords_ENT_ORG` is highly correlated with `word_count` and 3 other fields	High correlation
`TEXT_LeadParagraph_ENT_NORP` is highly correlated with `TEXT_LeadParagraph_POS_NOUN` and 1 other fields	High correlation
`TEXT_Keywords_ENT_FAC` is highly correlated with `TEXT_Keywords_ENT_GPE`	High correlation
`TEXT_LeadParagraph_ENT_GPE` is highly correlated with `TEXT_LeadParagraph_POS_PNOUN`	High correlation
`TEXT_Keywords_ENT_GPE` is highly correlated with `newsdesk` and 3 other fields	High correlation
`TEXT_Keywords_ENT_LOC` is highly correlated with `newsdesk`	High correlation
`TEXT_LeadParagraph_ENT_PERSON` is highly correlated with `TEXT_LeadParagraph_POS_PNOUN`	High correlation
`TEXT_Keyrwords_ENT_PERSON` is highly correlated with `word_count` and 2 other fields	High correlation
`print_section` has 252 (30.0%) missing values	Missing
`print_page` has 252 (30.0%) missing values	Missing
`headline` is uniformly distributed	Uniform
`abstract` is uniformly distributed	Uniform
`uniqueID` is uniformly distributed	Uniform
`lead_paragraph` is uniformly distributed	Uniform
`headline.main` is uniformly distributed	Uniform
`df_index` has unique values	Unique
`uniqueID` has unique values	Unique
`word_count` has 19 (2.3%) zeros	Zeros
`TEXT_LeadParagraph_POS_NOUN` has 27 (3.2%) zeros	Zeros
`TEXT_LeadParagraph_POS_PNOUN` has 130 (15.5%) zeros	Zeros
`TEXT_Keywords_POS_NOUN` has 116 (13.8%) zeros	Zeros
`TEXT_Keywords_POS_PNOUN` has 37 (4.4%) zeros	Zeros
`TEXT_headline.main_POS_PNOUN` has 56 (6.7%) zeros	Zeros
`TEXT_LeadParagraph_ENT_ORG` has 555 (66.2%) zeros	Zeros
`TEXT_Keywords_ENT_ORG` has 262 (31.2%) zeros	Zeros
`TEXT_LeadParagraph_ENT_NORP` has 720 (85.8%) zeros	Zeros
`TEXT_LeadParagraph_ENT_GPE` has 450 (53.6%) zeros	Zeros
`TEXT_Keywords_ENT_GPE` has 406 (48.4%) zeros	Zeros
`TEXT_LeadParagraph_ENT_PERSON` has 461 (54.9%) zeros	Zeros
`TEXT_Keyrwords_ENT_PERSON` has 262 (31.2%) zeros	Zeros

Reproduction

Analysis started	2022-01-11 22:26:53.386349
Analysis finished	2022-01-11 22:28:25.120151
Duration	1 minute and 31.73 seconds
Software version	pandas-profiling v3.1.0
Download configuration	config.json

df_index
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct	839
Distinct (%)	100.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	8483.678188

Minimum	7
Maximum	16771
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	6.7 KiB

Quantile statistics

Minimum	7
5-th percentile	862.7
Q1	4367.5
median	8275
Q3	12657
95-th percentile	15936
Maximum	16771
Range	16764
Interquartile range (IQR)	8289.5

Descriptive statistics

Standard deviation	4818.381176
Coefficient of variation (CV)	0.567958976
Kurtosis	-1.205589822
Mean	8483.678188
Median Absolute Deviation (MAD)	4139
Skewness	-0.02563644036
Sum	7117806
Variance	23216797.16
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
13715	1	0.1%
10178	1	0.1%
11257	1	0.1%
424	1	0.1%
27	1	0.1%
13900	1	0.1%
8274	1	0.1%
13698	1	0.1%
5656	1	0.1%
14638	1	0.1%
Other values (829)	829	98.8%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
7	1	0.1%
27	1	0.1%
32	1	0.1%
48	1	0.1%
55	1	0.1%
110	1	0.1%
118	1	0.1%
122	1	0.1%
180	1	0.1%
186	1	0.1%

Value	Count	Frequency (%)
16771	1	0.1%
16749	1	0.1%
16747	1	0.1%
16730	1	0.1%
16695	1	0.1%
16646	1	0.1%
16645	1	0.1%
16640	1	0.1%
16624	1	0.1%
16612	1	0.1%

newsdesk
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct	42
Distinct (%)	5.0%
Missing	0
Missing (%)	0.0%
Memory size	6.7 KiB

OpEd	98
Business	64
Foreign	54
Culture	48
Metro	47
Other values (37)	528

Length

Max length	15
Median length	7
Mean length	7.059594756
Min length	4

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	4 ?
Unique (%)	0.5%

Sample

1st row	Sports
2nd row	Well
3rd row	Upshot
4th row	Foreign
5th row	OpEd

Common Values

Value	Count	Frequency (%)
OpEd	98	11.7%
Business	64	7.6%
Foreign	54	6.4%
Culture	48	5.7%
Metro	47	5.6%
National	41	4.9%
Washington	39	4.6%
Learning	32	3.8%
Dining	31	3.7%
RealEstate	31	3.7%
Other values (32)	354	42.2%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
oped	98	11.6%
business	64	7.6%
foreign	54	6.4%
culture	48	5.7%
metro	47	5.6%
national	41	4.9%
washington	39	4.6%
learning	32	3.8%
dining	31	3.7%
realestate	31	3.7%
Other values (33)	359	42.5%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

section
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct	31
Distinct (%)	3.7%
Missing	0
Missing (%)	0.0%
Memory size	6.7 KiB

U.S.	116
Opinion	115
New York	63
World	60
Business Day	49
Other values (26)	436

Length

Max length	20
Median length	7
Mean length	7.483909416
Min length	4

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1 ?
Unique (%)	0.1%

Sample

1st row	Sports
2nd row	Well
3rd row	The Upshot
4th row	World
5th row	Opinion

Common Values

Value	Count	Frequency (%)
U.S.	116	13.8%
Opinion	115	13.7%
New York	63	7.5%
World	60	7.2%
Business Day	49	5.8%
Arts	48	5.7%
The Learning Network	34	4.1%
Food	32	3.8%
Real Estate	31	3.7%
Well	30	3.6%
Other values (21)	261	31.1%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
u.s	116	10.3%
opinion	115	10.2%
new	63	5.6%
york	63	5.6%
world	60	5.3%
business	49	4.3%
day	49	4.3%
the	49	4.3%
arts	48	4.2%
network	34	3.0%
Other values (31)	484	42.8%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

material
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct	8
Distinct (%)	1.0%
Missing	0
Missing (%)	0.0%
Memory size	6.7 KiB

News	653
Op-Ed	102
Review	28
Interactive Feature	19
briefing	13
Other values (3)	24

Length

Max length	19
Median length	4
Mean length	4.816448153
Min length	4

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	News
2nd row	News
3rd row	News
4th row	News
5th row	Op-Ed

Common Values

Value	Count	Frequency (%)
News	653	77.8%
Op-Ed	102	12.2%
Review	28	3.3%
Interactive Feature	19	2.3%
briefing	13	1.5%
Editorial	11	1.3%
Obituary (Obit)	9	1.1%
News Analysis	4	0.5%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
news	657	75.4%
op-ed	102	11.7%
review	28	3.2%
interactive	19	2.2%
feature	19	2.2%
briefing	13	1.5%
editorial	11	1.3%
obituary	9	1.0%
obit	9	1.0%
analysis	4	0.5%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

headline
Categorical

HIGH CARDINALITY
UNIFORM

Distinct	838
Distinct (%)	99.9%
Missing	0
Missing (%)	0.0%
Memory size	6.7 KiB

Homes for Sale in New York and Connecticut	2
Mookie Betts Leads Dodgers’ Stars With a Masterly Performance	1
Bill Gates Is the Most Interesting Man in the World	1
After 190 Years, the ‘Most Famous Bar You’ve Never Heard of’ Avoids Last Call	1
Already Had Plenty of Trump 2020?	1
Other values (833)	833

Length

Max length	112
Median length	57
Mean length	54.15852205
Min length	7

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	837 ?
Unique (%)	99.8%

Sample

1st row	Mookie Betts Leads Dodgers’ Stars With a Masterly Performance
2nd row	Taking Baths May Be Good for Your Heart
3rd row	Young Men Embrace Gender Equality, but They Still Don’t Vacuum
4th row	2 Weeks, 6.5 Million Coronavirus Tests as Wuhan Nears Goal
5th row	Is the Stock Market Rooting for Trump or Biden?

Common Values

Value	Count	Frequency (%)
Homes for Sale in New York and Connecticut	2	0.2%
Mookie Betts Leads Dodgers’ Stars With a Masterly Performance	1	0.1%
Bill Gates Is the Most Interesting Man in the World	1	0.1%
After 190 Years, the ‘Most Famous Bar You’ve Never Heard of’ Avoids Last Call	1	0.1%
Already Had Plenty of Trump 2020?	1	0.1%
How the Virus Slowed the Booming Wind Energy Business	1	0.1%
Coronavirus Cases Rise Sharply in Prisons Even as They Plateau Nationwide	1	0.1%
Museums Are Back, but Different: A Visitor’s Guide	1	0.1%
Jobless Numbers Are ‘Eye-Watering’ but Understate the Crisis	1	0.1%
For Veterans Day, Some Former Military Officers Reflect on Lessons From Their Parents	1	0.1%
Other values (828)	828	98.7%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
the	312	4.1%
a	209	2.7%
to	163	2.1%
in	145	1.9%
of	143	1.9%
and	117	1.5%
for	88	1.2%
is	84	1.1%
on	64	0.8%
coronavirus	56	0.7%
Other values (3094)	6225	81.8%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

abstract
Categorical

HIGH CARDINALITY
UNIFORM

Distinct	837
Distinct (%)	99.9%
Missing	1
Missing (%)	0.1%
Memory size	6.7 KiB

Look closely at this image, stripped of its caption, and join the moderated conversation about what you and other students see.	2
The Dodgers overwhelmed the Rays in Game 1, with Betts showing off the consistent excellence that led Los Angeles to sign him to a 12-year contract.	1
He’s everywhere, this lavender-sweatered Mister Rogers for the curious and quarantined.	1
The owner of Neir’s Tavern in Queens said it would close on Sunday, barring a “miracle.” The city, it turns out, works in mysterious ways.	1
He’s a bad show, but it’s not low-flow.	1
Other values (832)	832

Length

Max length	344
Median length	130
Mean length	126.8078759
Min length	23

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	836 ?
Unique (%)	99.8%

Sample

1st row	The Dodgers overwhelmed the Rays in Game 1, with Betts showing off the consistent excellence that led Los Angeles to sign him to a 12-year contract.
2nd row	Daily baths reduced the risk of heart disease and stroke.
3rd row	New studies show traditional views persist about who does what at home, and it’s holding women back.
4th row	The Chinese city where the outbreak began is seeking to test all its 11 million residents, and the pandemic has forced the fashion industry to take a hard look in the mirror.
5th row	Neither. Wall Street is not as partisan as you think.

Common Values

Value	Count	Frequency (%)
Look closely at this image, stripped of its caption, and join the moderated conversation about what you and other students see.	2	0.2%
The Dodgers overwhelmed the Rays in Game 1, with Betts showing off the consistent excellence that led Los Angeles to sign him to a 12-year contract.	1	0.1%
He’s everywhere, this lavender-sweatered Mister Rogers for the curious and quarantined.	1	0.1%
The owner of Neir’s Tavern in Queens said it would close on Sunday, barring a “miracle.” The city, it turns out, works in mysterious ways.	1	0.1%
He’s a bad show, but it’s not low-flow.	1	0.1%
Renewable energy developers have struggled to finish projects as the pandemic disrupts construction and global supply chains.	1	0.1%
Prison officials have been reluctant to do widespread virus testing even as infection rates are escalating.	1	0.1%
The visitors may be masked, but the art is gradually coming into full view.	1	0.1%
With 4.4 million added last week, the five-week total passed 26 million. The struggle by states to field claims has hampered economic recovery.	1	0.1%
The values that shaped them include leadership, optimism and charting your own course.	1	0.1%
Other values (827)	827	98.6%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
the	1065	6.1%
a	537	3.1%
to	471	2.7%
and	459	2.6%
of	454	2.6%
in	396	2.3%
is	184	1.1%
for	164	0.9%
are	141	0.8%
with	135	0.8%
Other values (5169)	13496	77.1%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

keywords
Categorical

HIGH CARDINALITY

Distinct	781
Distinct (%)	93.1%
Missing	0
Missing (%)	0.0%
Memory size	6.7 KiB

[]	33
['Crossword Puzzles']	13
['Coronavirus (2019-nCoV)']	10
['New York City']	4
['Trump, Donald J', 'Presidential Election of 2020', 'United States Politics and Government']	2
Other values (776)	777

Length

Max length	1381
Median length	159
Mean length	170.0059595
Min length	2

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	775 ?
Unique (%)	92.4%

Sample

1st row	['Baseball', 'World Series', 'Boston Red Sox', 'Los Angeles Dodgers', 'Tampa Bay Rays', 'Bellinger, Cody (1995- )', 'Betts, Mookie (1992- )', 'Kershaw, Clayton']
2nd row	['Bathing and Showering', 'Heart', 'Blood Pressure']
3rd row	['Work-Life Balance', 'Women and Girls', 'Research', 'Men and Boys', 'Parenting', 'Labor and Jobs', 'United States']
4th row	['Coronavirus (2019-nCoV)']
5th row	['Presidential Election of 2020', 'United States Economy', 'Stocks and Bonds']

Common Values

Value	Count	Frequency (%)
[]	33	3.9%
['Crossword Puzzles']	13	1.5%
['Coronavirus (2019-nCoV)']	10	1.2%
['New York City']	4	0.5%
['Trump, Donald J', 'Presidential Election of 2020', 'United States Politics and Government']	2	0.2%
['Television', 'Billions (TV Program)']	2	0.2%
['Bicycles and Bicycling', 'Women and Girls', 'Coronavirus (2019-nCoV)', 'Traffic Accidents and Safety', 'Commuting', 'Citi Bike', 'New York University', 'Transportation Alternatives', 'New York City']	1	0.1%
['Museums', 'Coronavirus Reopenings', 'AMERICAN MUSEUM OF NATURAL HISTORY', 'Dallas Museum of Art', 'Gardner, Isabella Stewart, Museum', 'Los Angeles County Museum of Art', 'Metropolitan Museum of Art', 'Museum of Fine Arts (Boston)', 'Museum of Modern Art', 'National Gallery of Art', 'Whitney Museum of American Art', 'Perez, Jorge M, Art Museum of Miami-Dade County', 'Museum of Fine Arts (Houston)', 'Cleveland Museum of Art', 'Sirmans, Franklin', 'Tinterow, Gary', 'Weinberg, Adam D', 'Zumthor, Peter']	1	0.1%
['Coronavirus Aid, Relief, and Economic Security Act (2020)', 'Coronavirus (2019-nCoV)', 'Unemployment', 'Unemployment Insurance', 'Layoffs and Job Reductions', 'Labor and Jobs', 'United States Economy', 'States (US)']	1	0.1%
['United States Defense and Military Forces', 'Children and Childhood', 'Careers and Professions', 'Parenting', 'Families and Family Life', 'Defense Department', 'United States Army', 'United States Marine Corps', 'United States Navy']	1	0.1%
Other values (771)	771	91.9%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
and	1310	7.9%
coronavirus	352	2.1%
2019-ncov	289	1.8%
states	277	1.7%
united	267	1.6%
of	228	1.4%
government	190	1.2%
politics	190	1.2%
j	150	0.9%
	150	0.9%
Other values (3593)	13090	79.4%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

word_count
Real number (ℝ_≥0)

HIGH CORRELATION
ZEROS

Distinct	642
Distinct (%)	76.5%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	1343.942789

Minimum	0
Maximum	11020
Zeros	19
Zeros (%)	2.3%
Negative	0
Negative (%)	0.0%
Memory size	6.7 KiB

Quantile statistics

Minimum	0
5-th percentile	268.7
Q1	917.5
median	1203
Q3	1497.5
95-th percentile	2539.2
Maximum	11020
Range	11020
Interquartile range (IQR)	580

Descriptive statistics

Standard deviation	1024.445849
Coefficient of variation (CV)	0.7622689428
Kurtosis	27.90786588
Mean	1343.942789
Median Absolute Deviation (MAD)	291
Skewness	4.331902981
Sum	1127568
Variance	1049489.297
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	19	2.3%
1439	5	0.6%
1226	5	0.6%
1270	4	0.5%
1026	4	0.5%
1369	4	0.5%
1424	3	0.4%
1048	3	0.4%
985	3	0.4%
1286	3	0.4%
Other values (632)	786	93.7%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	19	2.3%
10	1	0.1%
16	2	0.2%
74	1	0.1%
77	1	0.1%
115	1	0.1%
118	1	0.1%
133	1	0.1%
136	1	0.1%
137	1	0.1%

Value	Count	Frequency (%)
11020	1	0.1%
10329	1	0.1%
8801	1	0.1%
8223	1	0.1%
7815	1	0.1%
6701	1	0.1%
6522	1	0.1%
6325	1	0.1%
5919	1	0.1%
5894	1	0.1%

pub_date
Date

Distinct	834
Distinct (%)	99.4%
Missing	0
Missing (%)	0.0%
Memory size	6.7 KiB

Minimum	2020-01-01 10:00:01+00:00
Maximum	2020-12-31 10:01:02+00:00

Histogram

Histogram with fixed size bins (bins=50)

n_comments
Real number (ℝ_≥0)

HIGH CORRELATION

Distinct	408
Distinct (%)	48.6%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	309.0441001

Minimum	1
Maximum	5702
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	6.7 KiB

Quantile statistics

Minimum	1
5-th percentile	3
Q1	20
median	92
Q3	310.5
95-th percentile	1333.7
Maximum	5702
Range	5701
Interquartile range (IQR)	290.5

Descriptive statistics

Standard deviation	557.7115825
Coefficient of variation (CV)	1.804634297
Kurtosis	19.57107595
Mean	309.0441001
Median Absolute Deviation (MAD)	85
Skewness	3.744530566
Sum	259288
Variance	311042.2093
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
2	19	2.3%
8	18	2.1%
3	14	1.7%
12	14	1.7%
6	14	1.7%
1	13	1.5%
4	13	1.5%
7	11	1.3%
10	11	1.3%
15	10	1.2%
Other values (398)	702	83.7%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
1	13	1.5%
2	19	2.3%
3	14	1.7%
4	13	1.5%
5	8	1.0%
6	14	1.7%
7	11	1.3%
8	18	2.1%
9	10	1.2%
10	11	1.3%

Value	Count	Frequency (%)
5702	1	0.1%
3745	1	0.1%
3707	1	0.1%
3595	1	0.1%
3425	1	0.1%
3145	1	0.1%
3131	1	0.1%
2570	1	0.1%
2556	1	0.1%
2526	1	0.1%

uniqueID
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct	839
Distinct (%)	100.0%
Missing	0
Missing (%)	0.0%
Memory size	6.7 KiB

nyt://article/905b23aa-991f-5f02-9a7b-f0a09a7cba27	1
nyt://article/9872c5b0-6015-5c99-905f-7de9e3d5b107	1
nyt://article/e109ae37-4553-513c-b51e-5409f725716f	1
nyt://article/7c07b95a-1dd9-5b0d-b320-c84e1f2cc979	1
nyt://article/d5686346-4b09-56b4-89f3-fe1c9c291f13	1
Other values (834)	834

Length

Max length	54
Median length	50
Mean length	50.09058403
Min length	50

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	839 ?
Unique (%)	100.0%

Sample

1st row	nyt://article/905b23aa-991f-5f02-9a7b-f0a09a7cba27
2nd row	nyt://article/40e910f7-8d5f-5beb-9e7c-7ed27ac1597c
3rd row	nyt://article/d96041df-e929-51ad-8dd1-88bb84964888
4th row	nyt://article/4154be08-a296-5881-8342-e9f223dc484c
5th row	nyt://article/afed02eb-f035-542a-8791-c243893e65d7

Common Values

Value	Count	Frequency (%)
nyt://article/905b23aa-991f-5f02-9a7b-f0a09a7cba27	1	0.1%
nyt://article/9872c5b0-6015-5c99-905f-7de9e3d5b107	1	0.1%
nyt://article/e109ae37-4553-513c-b51e-5409f725716f	1	0.1%
nyt://article/7c07b95a-1dd9-5b0d-b320-c84e1f2cc979	1	0.1%
nyt://article/d5686346-4b09-56b4-89f3-fe1c9c291f13	1	0.1%
nyt://article/e7d0466b-8190-5e04-a0f8-a94d603bcf5d	1	0.1%
nyt://article/f9777004-2651-556d-86de-0e409359f58f	1	0.1%
nyt://article/a7c443c8-1e8f-5426-b6d6-54347c448ece	1	0.1%
nyt://article/57e0985e-90c8-5007-8cd0-527d7d64a5f8	1	0.1%
nyt://article/39ff4653-14ed-5b5f-b599-317f9fb44c1a	1	0.1%
Other values (829)	829	98.8%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
nyt://article/905b23aa-991f-5f02-9a7b-f0a09a7cba27	1	0.1%
nyt://article/8ec51655-7bc0-552c-bdc0-f620313e7e63	1	0.1%
nyt://article/42043d2b-a73c-5c31-ba4e-457392e735c2	1	0.1%
nyt://article/d96041df-e929-51ad-8dd1-88bb84964888	1	0.1%
nyt://article/4154be08-a296-5881-8342-e9f223dc484c	1	0.1%
nyt://article/afed02eb-f035-542a-8791-c243893e65d7	1	0.1%
nyt://article/80b0675a-7867-57f2-919e-8642e939f747	1	0.1%
nyt://article/0e73f3b9-b692-572f-85cd-6ab8831dfffc	1	0.1%
nyt://article/6ed0d121-21ca-54d5-b0cf-2e7cef434eb5	1	0.1%
nyt://article/ef8de76b-cb6b-55e2-9a1c-1e8165c20cb7	1	0.1%
Other values (829)	829	98.8%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

print_section
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct	16
Distinct (%)	2.7%
Missing	252
Missing (%)	30.0%
Memory size	6.7 KiB

A	289
B	82
D	64
C	45
SR	17
Other values (11)	90

Length

Max length	2
Median length	1
Mean length	1.170357751
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	B
2nd row	D
3rd row	B
4th row	A
5th row	A

Common Values

Value	Count	Frequency (%)
A	289	34.4%
B	82	9.8%
D	64	7.6%
C	45	5.4%
SR	17	2.0%
RE	16	1.9%
MM	15	1.8%
AR	13	1.5%
MB	13	1.5%
BR	11	1.3%
Other values (6)	22	2.6%
(Missing)	252	30.0%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
a	289	49.2%
b	82	14.0%
d	64	10.9%
c	45	7.7%
sr	17	2.9%
re	16	2.7%
mm	15	2.6%
ar	13	2.2%
mb	13	2.2%
br	11	1.9%
Other values (6)	22	3.7%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

print_page
Real number (ℝ_≥0)

HIGH CORRELATION
MISSING

Distinct	32
Distinct (%)	5.5%
Missing	252
Missing (%)	30.0%
Infinite	0
Infinite (%)	0.0%
Mean	8.991482112

Minimum	1
Maximum	36
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	6.7 KiB

Quantile statistics

Minimum	1
5-th percentile	1
Q1	1
median	6
Q3	16
95-th percentile	26
Maximum	36
Range	35
Interquartile range (IQR)	15

Descriptive statistics

Standard deviation	8.603808726
Coefficient of variation (CV)	0.9568843733
Kurtosis	-0.4803899181
Mean	8.991482112
Median Absolute Deviation (MAD)	5
Skewness	0.8861114949
Sum	5278
Variance	74.02552459
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=32)

Value	Count	Frequency (%)
1	158	18.8%
6	42	5.0%
2	39	4.6%
3	32	3.8%
4	32	3.8%
10	19	2.3%
5	19	2.3%
23	19	2.3%
12	18	2.1%
8	18	2.1%
Other values (22)	191	22.8%
(Missing)	252	30.0%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
1	158	18.8%
2	39	4.6%
3	32	3.8%
4	32	3.8%
5	19	2.3%
6	42	5.0%
7	17	2.0%
8	18	2.1%
9	15	1.8%
10	19	2.3%

Value	Count	Frequency (%)
36	1	0.1%
35	1	0.1%
30	4	0.5%
29	2	0.2%
28	2	0.2%
27	15	1.8%
26	9	1.1%
25	4	0.5%
24	7	0.8%
23	19	2.3%

lead_paragraph
Categorical

HIGH CARDINALITY
UNIFORM

Distinct	785
Distinct (%)	93.6%
Missing	0
Missing (%)	0.0%
Memory size	6.7 KiB

	15
[Want to get New York Today by email? Here’s the sign-up.]	12
Listen and subscribe to our podcast from your mobile device:Via Apple Podcasts \| Via Spotify \| Via Stitcher	9
Times Insider explains who we are and what we do, and delivers behind-the-scenes insights into how our journalism comes together.	5
Click on the slide show to see this week’s featured properties:	4
Other values (780)	794

Length

Max length	1162
Median length	232
Mean length	239.2443385
Min length	0

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	770 ?
Unique (%)	91.8%

Sample

1st row	ARLINGTON, Texas — Mookie Betts was 24 years old when he wondered if he would ever be any better. He had just finished as runner-up to Mike Trout for the American League Most Valuable Player Award in 2016, with a season full of hits and homers and steals and defensive excellence. What could he do next?
2nd row	Taking frequent baths may reduce the risk for cardiovascular disease, new research suggests.
3rd row	Young people today have become much more open-minded about gender roles — it shows up in their attitudes about pronouns, politics and sports. But in one area, change has been minimal. They are holding on to traditional views about who does what at home.
4th row	新冠病毒疫情最新消息
5th row	For months the S&P 500 rose this year — despite a deadly pandemic, the resulting economic devastation and the rise of a Democratic Party increasingly sympathetic to democratic socialism. Then, this month, with Joe Biden doing well in the polls, stock prices finally stumbled.

Common Values

Value	Count	Frequency (%)
	15	1.8%
[Want to get New York Today by email? Here’s the sign-up.]	12	1.4%
Listen and subscribe to our podcast from your mobile device:Via Apple Podcasts \| Via Spotify \| Via Stitcher	9	1.1%
Times Insider explains who we are and what we do, and delivers behind-the-scenes insights into how our journalism comes together.	5	0.6%
Click on the slide show to see this week’s featured properties:	4	0.5%
Dear Diary:	4	0.5%
This briefing has ended. Follow our latest coverage of the coronavirus pandemic.	3	0.4%
Welcome to Best of Late Night, a rundown of the previous night’s highlights that lets you sleep — and lets us get paid to watch comedy. We’re all stuck at home at the moment, so here are the 50 best movies on Netflix right now.	3	0.4%
[Follow the DNC Live: Biden’s speech, schedule, start time, streaming and more.]	2	0.2%
[Read our live updates on President Trump’s coronavirus diagnosis.]	2	0.2%
Other values (775)	780	93.0%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
the	1849	5.5%
a	1026	3.0%
of	940	2.8%
to	842	2.5%
and	828	2.4%
in	761	2.2%
on	354	1.0%
that	339	1.0%
—	317	0.9%
for	304	0.9%
Other values (7763)	26295	77.7%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

headline.main
Categorical

HIGH CARDINALITY
UNIFORM

Distinct	838
Distinct (%)	99.9%
Missing	0
Missing (%)	0.0%
Memory size	6.7 KiB

Homes for Sale in New York and Connecticut	2
Mookie Betts Leads Dodgers’ Stars With a Masterly Performance	1
Bill Gates Is the Most Interesting Man in the World	1
After 190 Years, the ‘Most Famous Bar You’ve Never Heard of’ Avoids Last Call	1
Already Had Plenty of Trump 2020?	1
Other values (833)	833

Length

Max length	112
Median length	57
Mean length	54.15852205
Min length	7

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	837 ?
Unique (%)	99.8%

Sample

1st row	Mookie Betts Leads Dodgers’ Stars With a Masterly Performance
2nd row	Taking Baths May Be Good for Your Heart
3rd row	Young Men Embrace Gender Equality, but They Still Don’t Vacuum
4th row	2 Weeks, 6.5 Million Coronavirus Tests as Wuhan Nears Goal
5th row	Is the Stock Market Rooting for Trump or Biden?

Common Values

Value	Count	Frequency (%)
Homes for Sale in New York and Connecticut	2	0.2%
Mookie Betts Leads Dodgers’ Stars With a Masterly Performance	1	0.1%
Bill Gates Is the Most Interesting Man in the World	1	0.1%
After 190 Years, the ‘Most Famous Bar You’ve Never Heard of’ Avoids Last Call	1	0.1%
Already Had Plenty of Trump 2020?	1	0.1%
How the Virus Slowed the Booming Wind Energy Business	1	0.1%
Coronavirus Cases Rise Sharply in Prisons Even as They Plateau Nationwide	1	0.1%
Museums Are Back, but Different: A Visitor’s Guide	1	0.1%
Jobless Numbers Are ‘Eye-Watering’ but Understate the Crisis	1	0.1%
For Veterans Day, Some Former Military Officers Reflect on Lessons From Their Parents	1	0.1%
Other values (828)	828	98.7%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
the	312	4.1%
a	209	2.7%
to	163	2.1%
in	145	1.9%
of	143	1.9%
and	117	1.5%
for	88	1.2%
is	84	1.1%
on	64	0.8%
coronavirus	56	0.7%
Other values (3094)	6225	81.8%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

month
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	12
Distinct (%)	1.4%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	6.383790226

Minimum	1
Maximum	12
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	6.7 KiB

Quantile statistics

Minimum	1
5-th percentile	1
Q1	3
median	6
Q3	9
95-th percentile	12
Maximum	12
Range	11
Interquartile range (IQR)	6

Descriptive statistics

Standard deviation	3.426420502
Coefficient of variation (CV)	0.5367376402
Kurtosis	-1.230186716
Mean	6.383790226
Median Absolute Deviation (MAD)	3
Skewness	0.07258068568
Sum	5356
Variance	11.74035745
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=12)

Value	Count	Frequency (%)
5	88	10.5%
4	83	9.9%
9	80	9.5%
3	79	9.4%
10	76	9.1%
12	67	8.0%
1	67	8.0%
2	66	7.9%
8	63	7.5%
6	62	7.4%
Other values (2)	108	12.9%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
1	67	8.0%
2	66	7.9%
3	79	9.4%
4	83	9.9%
5	88	10.5%
6	62	7.4%
7	50	6.0%
8	63	7.5%
9	80	9.5%
10	76	9.1%

Value	Count	Frequency (%)
12	67	8.0%
11	58	6.9%
10	76	9.1%
9	80	9.5%
8	63	7.5%
7	50	6.0%
6	62	7.4%
5	88	10.5%
4	83	9.9%
3	79	9.4%

Has_Comments
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct	1
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Memory size	6.7 KiB

1	839

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	1
2nd row	1
3rd row	1
4th row	1
5th row	1

Common Values

Value	Count	Frequency (%)
1	839	100.0%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
1	839	100.0%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

comment_size
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct	3
Distinct (%)	0.4%
Missing	0
Missing (%)	0.0%
Memory size	1.1 KiB

S	428
M	213
L	198

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	S
2nd row	S
3rd row	L
4th row	S
5th row	M

Common Values

Value	Count	Frequency (%)
S	428	51.0%
M	213	25.4%
L	198	23.6%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
s	428	51.0%
m	213	25.4%
l	198	23.6%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

TEXT_LeadParagraph_POS_NOUN
Real number (ℝ_≥0)

HIGH CORRELATION
ZEROS

Distinct	37
Distinct (%)	4.4%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	9.05363528

Minimum	0
Maximum	46
Zeros	27
Zeros (%)	3.2%
Negative	0
Negative (%)	0.0%
Memory size	6.7 KiB

Quantile statistics

Minimum	0
5-th percentile	1
Q1	5
median	9
Q3	12
95-th percentile	19
Maximum	46
Range	46
Interquartile range (IQR)	7

Descriptive statistics

Standard deviation	6.063564097
Coefficient of variation (CV)	0.6697380566
Kurtosis	4.505171217
Mean	9.05363528
Median Absolute Deviation (MAD)	4
Skewness	1.419551795
Sum	7596
Variance	36.76680956
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=37)

Value	Count	Frequency (%)
9	70	8.3%
11	61	7.3%
8	58	6.9%
4	56	6.7%
7	56	6.7%
6	56	6.7%
10	55	6.6%
12	54	6.4%
3	48	5.7%
5	42	5.0%
Other values (27)	283	33.7%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	27	3.2%
1	34	4.1%
2	39	4.6%
3	48	5.7%
4	56	6.7%
5	42	5.0%
6	56	6.7%
7	56	6.7%
8	58	6.9%
9	70	8.3%

Value	Count	Frequency (%)
46	1	0.1%
44	1	0.1%
40	1	0.1%
35	1	0.1%
34	1	0.1%
33	1	0.1%
31	2	0.2%
30	2	0.2%
29	1	0.1%
27	2	0.2%

TEXT_LeadParagraph_POS_PNOUN
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	21
Distinct (%)	2.5%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	4.468414779

Minimum	0
Maximum	24
Zeros	130
Zeros (%)	15.5%
Negative	0
Negative (%)	0.0%
Memory size	6.7 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	2
median	4
Q3	6
95-th percentile	12
Maximum	24
Range	24
Interquartile range (IQR)	4

Descriptive statistics

Standard deviation	3.787133252
Coefficient of variation (CV)	0.8475339554
Kurtosis	2.061353635
Mean	4.468414779
Median Absolute Deviation (MAD)	2
Skewness	1.226488208
Sum	3749
Variance	14.34237827
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=21)

Value	Count	Frequency (%)
0	130	15.5%
3	107	12.8%
2	102	12.2%
5	85	10.1%
4	84	10.0%
6	74	8.8%
1	57	6.8%
7	56	6.7%
8	32	3.8%
9	30	3.6%
Other values (11)	82	9.8%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	130	15.5%
1	57	6.8%
2	102	12.2%
3	107	12.8%
4	84	10.0%
5	85	10.1%
6	74	8.8%
7	56	6.7%
8	32	3.8%
9	30	3.6%

Value	Count	Frequency (%)
24	1	0.1%
21	1	0.1%
19	3	0.4%
17	3	0.4%
16	4	0.5%
15	8	1.0%
14	9	1.1%
13	8	1.0%
12	7	0.8%
11	13	1.5%

TEXT_Keywords_POS_NOUN
Real number (ℝ_≥0)

HIGH CORRELATION
ZEROS

Distinct	15
Distinct (%)	1.8%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	2.991656734

Minimum	0
Maximum	17
Zeros	116
Zeros (%)	13.8%
Negative	0
Negative (%)	0.0%
Memory size	6.7 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	1
median	3
Q3	4
95-th percentile	7
Maximum	17
Range	17
Interquartile range (IQR)	3

Descriptive statistics

Standard deviation	2.369994813
Coefficient of variation (CV)	0.7922014535
Kurtosis	1.980832925
Mean	2.991656734
Median Absolute Deviation (MAD)	2
Skewness	1.063436947
Sum	2510
Variance	5.616875414
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=15)

Value	Count	Frequency (%)
2	151	18.0%
1	140	16.7%
3	126	15.0%
0	116	13.8%
4	114	13.6%
5	72	8.6%
6	49	5.8%
7	31	3.7%
8	22	2.6%
9	8	1.0%
Other values (5)	10	1.2%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	116	13.8%
1	140	16.7%
2	151	18.0%
3	126	15.0%
4	114	13.6%
5	72	8.6%
6	49	5.8%
7	31	3.7%
8	22	2.6%
9	8	1.0%

Value	Count	Frequency (%)
17	1	0.1%
14	1	0.1%
13	1	0.1%
11	1	0.1%
10	6	0.7%
9	8	1.0%
8	22	2.6%
7	31	3.7%
6	49	5.8%
5	72	8.6%

TEXT_Keywords_POS_PNOUN
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	49
Distinct (%)	5.8%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	13.78307509

Minimum	0
Maximum	146
Zeros	37
Zeros (%)	4.4%
Negative	0
Negative (%)	0.0%
Memory size	6.7 KiB

Quantile statistics

Minimum	0
5-th percentile	2
Q1	7
median	12
Q3	19
95-th percentile	30
Maximum	146
Range	146
Interquartile range (IQR)	12

Descriptive statistics

Standard deviation	10.68615635
Coefficient of variation (CV)	0.7753100292
Kurtosis	32.24330063
Mean	13.78307509
Median Absolute Deviation (MAD)	6
Skewness	3.515649292
Sum	11564
Variance	114.1939375
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=49)

Value	Count	Frequency (%)
6	44	5.2%
9	44	5.2%
12	43	5.1%
2	42	5.0%
10	40	4.8%
0	37	4.4%
8	36	4.3%
13	35	4.2%
7	35	4.2%
5	32	3.8%
Other values (39)	451	53.8%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	37	4.4%
1	4	0.5%
2	42	5.0%
3	16	1.9%
4	28	3.3%
5	32	3.8%
6	44	5.2%
7	35	4.2%
8	36	4.3%
9	44	5.2%

Value	Count	Frequency (%)
146	1	0.1%
83	1	0.1%
81	1	0.1%
73	1	0.1%
57	1	0.1%
53	1	0.1%
51	1	0.1%
45	1	0.1%
44	1	0.1%
41	2	0.2%

TEXT_headline.main_POS_NOUN
Categorical

HIGH CORRELATION

Distinct	5
Distinct (%)	0.6%
Missing	0
Missing (%)	0.0%
Memory size	6.7 KiB

0	354
1	327
2	121
3	31
4	6

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	2
2nd row	2
3rd row	0
4th row	2
5th row	0

Common Values

Value	Count	Frequency (%)
0	354	42.2%
1	327	39.0%
2	121	14.4%
3	31	3.7%
4	6	0.7%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	354	42.2%
1	327	39.0%
2	121	14.4%
3	31	3.7%
4	6	0.7%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

TEXT_headline.main_POS_PNOUN
Real number (ℝ_≥0)

ZEROS

Distinct	11
Distinct (%)	1.3%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	3.567342074

Minimum	0
Maximum	10
Zeros	56
Zeros (%)	6.7%
Negative	0
Negative (%)	0.0%
Memory size	6.7 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	2
median	3
Q3	5
95-th percentile	7
Maximum	10
Range	10
Interquartile range (IQR)	3

Descriptive statistics

Standard deviation	2.030185447
Coefficient of variation (CV)	0.569103104
Kurtosis	-0.3618087253
Mean	3.567342074
Median Absolute Deviation (MAD)	1
Skewness	0.2616108381
Sum	2993
Variance	4.121652951
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=11)

Value	Count	Frequency (%)
3	153	18.2%
2	144	17.2%
4	136	16.2%
5	132	15.7%
6	75	8.9%
1	74	8.8%
0	56	6.7%
7	43	5.1%
8	19	2.3%
9	5	0.6%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	56	6.7%
1	74	8.8%
2	144	17.2%
3	153	18.2%
4	136	16.2%
5	132	15.7%
6	75	8.9%
7	43	5.1%
8	19	2.3%
9	5	0.6%

Value	Count	Frequency (%)
10	2	0.2%
9	5	0.6%
8	19	2.3%
7	43	5.1%
6	75	8.9%
5	132	15.7%
4	136	16.2%
3	153	18.2%
2	144	17.2%
1	74	8.8%

TEXT_LeadParagraph_ENT_ORG
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	6
Distinct (%)	0.7%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	0.4541120381

Minimum	0
Maximum	5
Zeros	555
Zeros (%)	66.2%
Negative	0
Negative (%)	0.0%
Memory size	6.7 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	0
Q3	1
95-th percentile	2
Maximum	5
Range	5
Interquartile range (IQR)	1

Descriptive statistics

Standard deviation	0.7488920324
Coefficient of variation (CV)	1.649134948
Kurtosis	5.213057557
Mean	0.4541120381
Median Absolute Deviation (MAD)	0
Skewness	2.028195752
Sum	381
Variance	0.5608392762
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=6)

Value	Count	Frequency (%)
0	555	66.2%
1	212	25.3%
2	55	6.6%
3	10	1.2%
4	6	0.7%
5	1	0.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	555	66.2%
1	212	25.3%
2	55	6.6%
3	10	1.2%
4	6	0.7%
5	1	0.1%

Value	Count	Frequency (%)
5	1	0.1%
4	6	0.7%
3	10	1.2%
2	55	6.6%
1	212	25.3%
0	555	66.2%

TEXT_Keywords_ENT_ORG
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	13
Distinct (%)	1.5%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	1.497020262

Minimum	0
Maximum	14
Zeros	262
Zeros (%)	31.2%
Negative	0
Negative (%)	0.0%
Memory size	6.7 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	1
Q3	2
95-th percentile	4
Maximum	14
Range	14
Interquartile range (IQR)	2

Descriptive statistics

Standard deviation	1.633386218
Coefficient of variation (CV)	1.09109159
Kurtosis	7.991717462
Mean	1.497020262
Median Absolute Deviation (MAD)	1
Skewness	2.087866594
Sum	1256
Variance	2.667950538
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=13)

Value	Count	Frequency (%)
0	262	31.2%
1	240	28.6%
2	161	19.2%
3	106	12.6%
4	29	3.5%
5	22	2.6%
6	6	0.7%
7	4	0.5%
8	3	0.4%
9	3	0.4%
Other values (3)	3	0.4%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	262	31.2%
1	240	28.6%
2	161	19.2%
3	106	12.6%
4	29	3.5%
5	22	2.6%
6	6	0.7%
7	4	0.5%
8	3	0.4%
9	3	0.4%

Value	Count	Frequency (%)
14	1	0.1%
11	1	0.1%
10	1	0.1%
9	3	0.4%
8	3	0.4%
7	4	0.5%
6	6	0.7%
5	22	2.6%
4	29	3.5%
3	106	12.6%

TEXT_LeadParagraph_ENT_NORP
Real number (ℝ_≥0)

HIGH CORRELATION
ZEROS

Distinct	6
Distinct (%)	0.7%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	0.1811680572

Minimum	0
Maximum	5
Zeros	720
Zeros (%)	85.8%
Negative	0
Negative (%)	0.0%
Memory size	6.7 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	0
Q3	0
95-th percentile	1
Maximum	5
Range	5
Interquartile range (IQR)	0

Descriptive statistics

Standard deviation	0.5129148944
Coefficient of variation (CV)	2.83115524
Kurtosis	21.02227048
Mean	0.1811680572
Median Absolute Deviation (MAD)	0
Skewness	3.910939011
Sum	152
Variance	0.2630816889
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=6)

Value	Count	Frequency (%)
0	720	85.8%
1	96	11.4%
2	17	2.0%
3	3	0.4%
4	2	0.2%
5	1	0.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	720	85.8%
1	96	11.4%
2	17	2.0%
3	3	0.4%
4	2	0.2%
5	1	0.1%

Value	Count	Frequency (%)
5	1	0.1%
4	2	0.2%
3	3	0.4%
2	17	2.0%
1	96	11.4%
0	720	85.8%

TEXT_Keywords_ENT_NORP
Categorical

HIGH CORRELATION

Distinct	3
Distinct (%)	0.4%
Missing	0
Missing (%)	0.0%
Memory size	6.7 KiB

0	785
1	52
2	2

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	0
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	785	93.6%
1	52	6.2%
2	2	0.2%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	785	93.6%
1	52	6.2%
2	2	0.2%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

TEXT_LeadParagraph_ENT_FAC
Categorical

HIGH CORRELATION

Distinct	3
Distinct (%)	0.4%
Missing	0
Missing (%)	0.0%
Memory size	6.7 KiB

0	806
1	31
2	2

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	0
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	806	96.1%
1	31	3.7%
2	2	0.2%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	806	96.1%
1	31	3.7%
2	2	0.2%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

TEXT_Keywords_ENT_FAC
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	0.2%
Missing	0
Missing (%)	0.0%
Memory size	6.7 KiB

0	831
1	8

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	0
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	831	99.0%
1	8	1.0%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	831	99.0%
1	8	1.0%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

TEXT_LeadParagraph_ENT_GPE
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	7
Distinct (%)	0.8%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	0.7604290822

Minimum	0
Maximum	7
Zeros	450
Zeros (%)	53.6%
Negative	0
Negative (%)	0.0%
Memory size	6.7 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	0
Q3	1
95-th percentile	3
Maximum	7
Range	7
Interquartile range (IQR)	1

Descriptive statistics

Standard deviation	1.07079156
Coefficient of variation (CV)	1.408141252
Kurtosis	5.343947323
Mean	0.7604290822
Median Absolute Deviation (MAD)	0
Skewness	1.955458291
Sum	638
Variance	1.146594565
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=7)

Value	Count	Frequency (%)
0	450	53.6%
1	235	28.0%
2	95	11.3%
3	38	4.5%
4	12	1.4%
5	6	0.7%
7	3	0.4%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	450	53.6%
1	235	28.0%
2	95	11.3%
3	38	4.5%
4	12	1.4%
5	6	0.7%
7	3	0.4%

Value	Count	Frequency (%)
7	3	0.4%
5	6	0.7%
4	12	1.4%
3	38	4.5%
2	95	11.3%
1	235	28.0%
0	450	53.6%

TEXT_Keywords_ENT_GPE
Real number (ℝ_≥0)

HIGH CORRELATION
ZEROS

Distinct	11
Distinct (%)	1.3%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	0.8915375447

Minimum	0
Maximum	24
Zeros	406
Zeros (%)	48.4%
Negative	0
Negative (%)	0.0%
Memory size	6.7 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	1
Q3	1
95-th percentile	3
Maximum	24
Range	24
Interquartile range (IQR)	1

Descriptive statistics

Standard deviation	1.480634707
Coefficient of variation (CV)	1.6607654
Kurtosis	83.79575575
Mean	0.8915375447
Median Absolute Deviation (MAD)	1
Skewness	6.762831153
Sum	748
Variance	2.192279137
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=11)

Value	Count	Frequency (%)
0	406	48.4%
1	257	30.6%
2	115	13.7%
3	40	4.8%
4	11	1.3%
7	4	0.5%
5	2	0.2%
14	1	0.1%
13	1	0.1%
24	1	0.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	406	48.4%
1	257	30.6%
2	115	13.7%
3	40	4.8%
4	11	1.3%
5	2	0.2%
7	4	0.5%
8	1	0.1%
13	1	0.1%
14	1	0.1%

Value	Count	Frequency (%)
24	1	0.1%
14	1	0.1%
13	1	0.1%
8	1	0.1%
7	4	0.5%
5	2	0.2%
4	11	1.3%
3	40	4.8%
2	115	13.7%
1	257	30.6%

TEXT_LeadParagraph_ENT_LOC
Categorical

HIGH CORRELATION

Distinct	4
Distinct (%)	0.5%
Missing	0
Missing (%)	0.0%
Memory size	6.7 KiB

0	792
1	42
2	4
3	1

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1 ?
Unique (%)	0.1%

Sample

1st row	0
2nd row	0
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	792	94.4%
1	42	5.0%
2	4	0.5%
3	1	0.1%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	792	94.4%
1	42	5.0%
2	4	0.5%
3	1	0.1%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

TEXT_Keywords_ENT_LOC
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct	3
Distinct (%)	0.4%
Missing	0
Missing (%)	0.0%
Memory size	6.7 KiB

0	807
1	29
2	3

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	0
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	807	96.2%
1	29	3.5%
2	3	0.4%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	807	96.2%
1	29	3.5%
2	3	0.4%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

TEXT_LeadParagraph_ENT_PERSON
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	7
Distinct (%)	0.8%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	0.6507747318

Minimum	0
Maximum	6
Zeros	461
Zeros (%)	54.9%
Negative	0
Negative (%)	0.0%
Memory size	6.7 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	0
Q3	1
95-th percentile	2
Maximum	6
Range	6
Interquartile range (IQR)	1

Descriptive statistics

Standard deviation	0.9065375107
Coefficient of variation (CV)	1.393012768
Kurtosis	4.91928856
Mean	0.6507747318
Median Absolute Deviation (MAD)	0
Skewness	1.882145386
Sum	546
Variance	0.8218102583
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=7)

Value	Count	Frequency (%)
0	461	54.9%
1	263	31.3%
2	82	9.8%
3	20	2.4%
4	7	0.8%
5	5	0.6%
6	1	0.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	461	54.9%
1	263	31.3%
2	82	9.8%
3	20	2.4%
4	7	0.8%
5	5	0.6%
6	1	0.1%

Value	Count	Frequency (%)
6	1	0.1%
5	5	0.6%
4	7	0.8%
3	20	2.4%
2	82	9.8%
1	263	31.3%
0	461	54.9%

TEXT_Keyrwords_ENT_PERSON
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	14
Distinct (%)	1.7%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	1.887961859

Minimum	0
Maximum	18
Zeros	262
Zeros (%)	31.2%
Negative	0
Negative (%)	0.0%
Memory size	6.7 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	1
Q3	3
95-th percentile	6
Maximum	18
Range	18
Interquartile range (IQR)	3

Descriptive statistics

Standard deviation	2.256644502
Coefficient of variation (CV)	1.195280768
Kurtosis	8.863595042
Mean	1.887961859
Median Absolute Deviation (MAD)	1
Skewness	2.305513681
Sum	1584
Variance	5.092444409
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=14)

Value	Count	Frequency (%)
0	262	31.2%
1	208	24.8%
2	139	16.6%
3	74	8.8%
4	67	8.0%
5	34	4.1%
6	24	2.9%
7	9	1.1%
10	6	0.7%
8	5	0.6%
Other values (4)	11	1.3%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	262	31.2%
1	208	24.8%
2	139	16.6%
3	74	8.8%
4	67	8.0%
5	34	4.1%
6	24	2.9%
7	9	1.1%
8	5	0.6%
9	4	0.5%

Value	Count	Frequency (%)
18	2	0.2%
15	1	0.1%
11	4	0.5%
10	6	0.7%
9	4	0.5%
8	5	0.6%
7	9	1.1%
6	24	2.9%
5	34	4.1%
4	67	8.0%

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

A simple visualization of nullity by column.

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

First rows

	df_index	newsdesk	section	material	headline	abstract	keywords	word_count	pub_date	n_comments	uniqueID	print_section	print_page	lead_paragraph	headline.main	month	Has_Comments	comment_size	TEXT_LeadParagraph_POS_NOUN	TEXT_LeadParagraph_POS_PNOUN	TEXT_Keywords_POS_NOUN	TEXT_Keywords_POS_PNOUN	TEXT_headline.main_POS_NOUN	TEXT_headline.main_POS_PNOUN	TEXT_LeadParagraph_ENT_ORG	TEXT_Keywords_ENT_ORG	TEXT_LeadParagraph_ENT_FAC	TEXT_LeadParagraph_ENT_GPE	TEXT_Keywords_ENT_GPE	TEXT_LeadParagraph_ENT_PERSON	TEXT_Keyrwords_ENT_PERSON
0	13715	Sports	Sports	News	Mookie Betts Leads Dodgers’ Stars With a Masterly Performance	The Dodgers overwhelmed the Rays in Game 1, with Betts showing off the consistent excellence that led Los Angeles to sign him to a 12-year contract.	['Baseball', 'World Series', 'Boston Red Sox', 'Los Angeles Dodgers', 'Tampa Bay Rays', 'Bellinger, Cody (1995- )', 'Betts, Mookie (1992- )', 'Kershaw, Clayton']	1083	2020-10-21 13:23:08+00:00	18	nyt://article/905b23aa-991f-5f02-9a7b-f0a09a7cba27	B	10	ARLINGTON, Texas — Mookie Betts was 24 years old when he wondered if he would ever be any better. He had just finished as runner-up to Mike Trout for the American League Most Valuable Player Award in 2016, with a season full of hits and homers and steals and defensive excellence. What could he do next?	Mookie Betts Leads Dodgers’ Stars With a Masterly Performance	10	1	S	8	12	3	17	2	4	1	3	0	2	0	2	3
1	4455	Well	Well	News	Taking Baths May Be Good for Your Heart	Daily baths reduced the risk of heart disease and stroke.	['Bathing and Showering', 'Heart', 'Blood Pressure']	242	2020-04-01 16:34:18+00:00	14	nyt://article/40e910f7-8d5f-5beb-9e7c-7ed27ac1597c	D	6	Taking frequent baths may reduce the risk for cardiovascular disease, new research suggests.	Taking Baths May Be Good for Your Heart	4	1	S	4	0	3	2	2	0	0	0	0	0	0	0	0
2	1886	Upshot	The Upshot	News	Young Men Embrace Gender Equality, but They Still Don’t Vacuum	New studies show traditional views persist about who does what at home, and it’s holding women back.	['Work-Life Balance', 'Women and Girls', 'Research', 'Men and Boys', 'Parenting', 'Labor and Jobs', 'United States']	1315	2020-02-11 10:00:12+00:00	632	nyt://article/d96041df-e929-51ad-8dd1-88bb84964888	B	5	Young people today have become much more open-minded about gender roles — it shows up in their attitudes about pronouns, politics and sports. But in one area, change has been minimal. They are holding on to traditional views about who does what at home.	Young Men Embrace Gender Equality, but They Still Don’t Vacuum	2	1	L	12	0	8	5	0	4	0	0	0	0	1	0	0
3	7305	Foreign	World	News	2 Weeks, 6.5 Million Coronavirus Tests as Wuhan Nears Goal	The Chinese city where the outbreak began is seeking to test all its 11 million residents, and the pandemic has forced the fashion industry to take a hard look in the mirror.	['Coronavirus (2019-nCoV)']	3805	2020-05-26 04:05:17+00:00	61	nyt://article/4154be08-a296-5881-8342-e9f223dc484c	NaN	NaN	新冠病毒疫情最新消息	2 Weeks, 6.5 Million Coronavirus Tests as Wuhan Nears Goal	5	1	S	0	1	0	2	2	4	0	0	0	0	0	0	0
4	12331	OpEd	Opinion	Op-Ed	Is the Stock Market Rooting for Trump or Biden?	Neither. Wall Street is not as partisan as you think.	['Presidential Election of 2020', 'United States Economy', 'Stocks and Bonds']	880	2020-09-21 09:00:11+00:00	317	nyt://article/afed02eb-f035-542a-8791-c243893e65d7	A	23	For months the S&P 500 rose this year — despite a deadly pandemic, the resulting economic devastation and the rise of a Democratic Party increasingly sympathetic to democratic socialism. Then, this month, with Joe Biden doing well in the polls, stock prices finally stumbled.	Is the Stock Market Rooting for Trump or Biden?	9	1	M	10	5	3	5	0	5	1	1	0	0	0	1	0
5	6152	Politics	U.S.	News	Why Biden’s Choice of Running Mate Has Momentous Implications	Joe Biden has hinted that he might serve only one term if he wins. That would set up a woman as the front-runner for 2024 and perhaps define the Democratic agenda for the next decade.	['Presidential Election of 2020', 'Vice Presidents and Vice Presidency (US)', 'United States Politics and Government', 'Presidential Election of 2024', 'Biden, Joseph R Jr', 'Women and Girls', 'Democratic Party', 'Abrams, Stacey Y', 'Demings, Val', 'Grisham, Michelle Lujan', 'Harris, Kamala D', 'Klobuchar, Amy', 'Warren, Elizabeth', 'Rice, Susan E']	1725	2020-05-03 17:48:35+00:00	290	nyt://article/80b0675a-7867-57f2-919e-8642e939f747	A	1	WASHINGTON — For decades the vice-presidential selection process has had an air of cloak-and-dagger to it. The party’s nominees would say little about their thinking, the would-be running mates would reveal even less, and an elaborate game of subterfuge would unfold that mostly captivated political insiders and usually had little bearing on the election.	Why Biden’s Choice of Running Mate Has Momentous Implications	5	1	M	16	1	2	38	1	4	0	2	0	1	1	0	10
6	14233	Learning	The Learning Network	News	Lesson of the Day: ‘Lil Buck Feels the Dancing Spirit All Over Again’	In this lesson, students watch a short film by the dancer Lil Buck, then consider his contention that street dance is fine art and can be “a tool to help bring change.”	[]	788	2020-11-02 10:00:03+00:00	6	nyt://article/0e73f3b9-b692-572f-85cd-6ab8831dfffc	NaN	NaN	Featured Article: “Lil Buck Feels the Dancing Spirit All Over Again,” by Gia Kourlas	Lesson of the Day: ‘Lil Buck Feels the Dancing Spirit All Over Again’	11	1	S	1	6	0	0	0	6	0	0	1	0	0	1	0
7	15496	Styles	Fashion & Style	News	Alicia Keys Figures Out Her Skin	The singer-songwriter talks about finding her rituals (and learning how to deal with carbs).	['Keys, Alicia', 'Keys Soulcare (ELF Cosmetics Inc)', 'Cosmetics and Toiletries', 'Skin', 'Exercise', 'Meditation', 'Quarantine (Life and Culture)', 'Content Type: Personal Profile', 'Rhythm and Blues (Music)']	1226	2020-12-01 17:13:41+00:00	16	nyt://article/6ed0d121-21ca-54d5-b0cf-2e7cef434eb5	D	3		Alicia Keys Figures Out Her Skin	12	1	S	0	0	6	16	1	2	0	1	0	0	0	0	3
8	10579	Weekend	Books	News	She Explains ‘Mansplaining’ With Help From 17th-Century Art	In her new book “Men to Avoid in Art and Life,” Nicole Tersigni harnesses her skill with a Twitter meme to illuminate the experience of women harassed by concern trolls, “sexperts” and more.	['Books and Literature', 'Women and Girls', 'Comedy and Humor', 'Social Media', 'Writing and Writers', 'Art', 'Discrimination', 'Tersigni, Nicole', 'Men to Avoid in Art and Life (Book)']	893	2020-08-10 09:00:25+00:00	1523	nyt://article/ef8de76b-cb6b-55e2-9a1c-1e8165c20cb7	C	12	This story begins, as so many do these days, on Twitter.	She Explains ‘Mansplaining’ With Help From 17th-Century Art	8	1	L	2	1	8	10	3	1	0	2	0	0	0	0	2
9	6338	Podcasts	Podcasts	News	A Socially Distanced Senate	The two chambers of Congress received the same medical advice about reconvening. They took different decisions.	['United States Politics and Government', 'Coronavirus (2019-nCoV)', 'House of Representatives', 'Senate', 'Quarantines', 'Washington (DC)', 'Capitol Building (Washington, DC)']	284	2020-05-06 09:59:07+00:00	6	nyt://article/f229cb93-f6ab-5bef-bac4-89f3449d1721	NaN	NaN	Listen and subscribe to our podcast from your mobile device:Via Apple Podcasts \| Via Spotify \| Via Stitcher	A Socially Distanced Senate	5	1	S	4	6	1	15	0	2	0	3	0	0	2	0	1

Last rows

	df_index	newsdesk	section	material	headline	abstract	keywords	word_count	pub_date	n_comments	uniqueID	print_section	print_page	lead_paragraph	headline.main	month	Has_Comments	comment_size	TEXT_LeadParagraph_POS_NOUN	TEXT_LeadParagraph_POS_PNOUN	TEXT_Keywords_POS_NOUN	TEXT_Keywords_POS_PNOUN	TEXT_headline.main_POS_NOUN	TEXT_headline.main_POS_PNOUN	TEXT_LeadParagraph_ENT_ORG	TEXT_Keywords_ENT_ORG	TEXT_LeadParagraph_ENT_GPE	TEXT_Keywords_ENT_GPE	TEXT_LeadParagraph_ENT_PERSON	TEXT_Keyrwords_ENT_PERSON
829	16119	Travel	Travel	News	How to Pretend You’re in Singapore Tonight	You can feel like you are in the Lion City with a little work in the kitchen, the right book and some time in front of the TV.	['Cooking and Cookbooks', 'Movies', 'Travel and Vacations', 'Food', 'Television', 'Restaurants', 'Books and Literature', 'Bourdain, Anthony', 'Kwan, Kevin', 'Liew, Sonny', 'Singapore']	1432	2020-12-15 10:00:25+00:00	64	nyt://article/a511396b-640b-5be3-92e6-b0497bf6611e	NaN	NaN	It took over a dozen visits to Singapore for me to fall in love with it. But when I did, I fell hard. As a teenager living in Jakarta, Indonesia — just under two hours away by direct flight — I looked at Singapore’s shiny veneer and dismissed the whole place as shallow and materialistic. It was one big shopping mall, I thought, with too many rules and not enough character. But then, as I kept going back, I intentionally squashed my preconceptions and I started noticing other things. I quickly realized how much I had been missing.	How to Pretend You’re in Singapore Tonight	12	1	S	14	4	5	11	0	2	0	2	3	1	0	2
830	12843	Washington	U.S.	News	Trump Tests Positive for the Coronavirus	The president’s result came after he spent months playing down the severity of the outbreak that has killed more than 207,000 in the United States and hours after insisting that “the end of the pandemic is in sight.”	['Trump, Donald J', 'Coronavirus (2019-nCoV)', 'United States Politics and Government', 'Presidents and Presidency (US)', 'Twenty-Fifth Amendment (US Constitution)', 'Presidential Election of 2020', 'White House Coronavirus Outbreak (2020)']	1914	2020-10-02 05:01:16+00:00	3707	nyt://article/de217dd9-3383-574a-9eaf-c0303570e794	NaN	NaN	[Read our live updates on President Trump’s coronavirus diagnosis.]	Trump Tests Positive for the Coronavirus	10	1	L	2	3	1	22	0	3	0	1	0	1	1	2
831	6238	Metro	New York	News	2 Die From the Virus at a Bronx Bus Depot, and Drivers Are Rattled	On the front line of the city’s battle against the pandemic, bus drivers are struggling to come to terms with their role as essential workers.	['Buses', 'Coronavirus (2019-nCoV)', 'Transit Systems', 'Bronx (NYC)', 'Workplace Hazards and Violations', 'Metropolitan Transportation Authority', 'New York City', 'New York City Transit Authority']	1686	2020-05-05 07:00:11+00:00	187	nyt://article/769ee82a-293d-5203-bc8c-e37cae61c6c3	A	1	Angel Volquez was already on edge. For weeks, the New York City bus driver had watched as the city’s grim new reality played out in front of him through the large glass windshield like a dystopian movie.	2 Die From the Virus at a Bronx Bus Depot, and Drivers Are Rattled	5	1	M	10	5	1	20	1	4	0	3	1	3	1	2
832	6615	Learning	The Learning Network	News	Should Students Be Monitored When Taking Online Tests?	Is surveillance necessary to prevent students from cheating during online exams, or does it violate students’ privacy?	[]	764	2020-05-12 09:00:05+00:00	301	nyt://article/20c9eb36-46a9-565f-ba13-22e737a72eb9	NaN	NaN	Has cheating on tests ever been a problem at your school? What about now that school has gone online? What steps do you think teachers and professors should take to ensure that students are completing online exams honestly? Is there a point when online surveillance impedes on students’ privacy — or even becomes “creepy”?	Should Students Be Monitored When Taking Online Tests?	5	1	M	14	1	0	0	1	2	0	0	0	0	0	0
833	14143	OpEd	Opinion	Op-Ed	The Woman President Who Wasn’t	Trump may have beaten Hillary Clinton, but the story doesn’t end there.	['Presidential Election of 2020', 'Women and Girls', 'United States Politics and Government', 'Elections, Senate', "Women's Rights", 'Biden, Joseph R Jr', 'Senate', 'Trump, Donald J', 'Clinton, Hillary Rodham']	911	2020-10-30 09:00:14+00:00	171	nyt://article/d90c1075-cad0-55e1-bd3b-015f40de473e	SR	11	One of my clearest memories of election night in 2016 is running into women who were going to watch the results with their daughters, so they’d get to share the experience of seeing Hillary Clinton elected president — the moment when the political glass ceiling in America would be shattered forever.	The Woman President Who Wasn’t	10	1	M	11	3	4	20	0	2	0	2	1	0	1	6
834	1518	Foreign	World	News	Beijing in the Time of Coronavirus: No Traffic, Empty Parks and Fear	The Chinese capital, like other cities far from the epidemic’s center, has imposed restrictions and shut down public spaces, straining the ties that bind society.	['Coronavirus (2019-nCoV)', 'Beijing (China)', 'Shopping and Retail', 'Politics and Government', 'Epidemics', 'Parks and Other Recreation Areas', 'Communist Party of China', 'China']	1273	2020-02-03 16:06:58+00:00	18	nyt://article/40edd806-d950-5b6f-a751-d7ddbc3e9f92	A	7	BEIJING — The Apple stores were among the busiest places still open in Beijing after the coronavirus outbreak, though employees forbade customers to try the watches or AirPods.	Beijing in the Time of Coronavirus: No Traffic, Empty Parks and Fear	2	1	S	7	4	4	13	1	5	1	1	2	2	0	1
835	9970	Books	Books	News	The Celebrity Bookshelf Detective Is Back	We peer over the shoulders of Gwyneth Paltrow, Regina King, Charlamagne tha God, Yo-Yo Ma and others for a glimpse at their reading habits.	['Celebrities', 'Quarantine (Life and Culture)', 'Books and Literature', 'Videophones and Videoconferencing', 'Hanks, Tom', 'LuPone, Patti', 'Penn, Sean', 'Paltrow, Gwyneth', 'Charlamagne Tha God', 'Powell, Colin L', 'King, Regina (1971- )', 'Ma, Yo-Yo']	1094	2020-07-27 09:00:25+00:00	156	nyt://article/a4c60180-9a43-5fca-8109-8d5c3bc72787	BR	27	Zoom happy hours are far less exciting now than they were in March. So is sourdough starter. A lot of the early preoccupations of our lockdown life don’t quite have the same charm anymore. (Remember the night we all made lasagna together?) But the chance to speculate about the minds and proclivities of the famous by gawking at their bookshelves never gets old. So after a first foray into bookshelf sleuthing, we’re back for more.	The Celebrity Bookshelf Detective Is Back	7	1	M	15	2	3	25	0	3	0	3	0	1	0	4
836	5899	Foreign	World	News	Genoa’s New Bridge Nears Completion, Turning Tragedy Into Hope	Nearly two years after 43 people died when a bridge collapsed, its replacement, built in record time, has become a symbol of Italian can-do.	['Morandi Bridge (Genoa, Italy)', 'Bridges and Tunnels', 'Infrastructure (Public Works)', 'Piano, Renzo', 'Roads and Traffic', 'Conte, Giuseppe', 'Salini Impregilo SpA', 'Fincantieri', 'Genoa (Italy)']	1164	2020-04-28 14:23:28+00:00	56	nyt://article/fd07ece0-584c-5199-bad0-177ff5cc972e	A	18	ROME — When the Morandi Bridge, a vital east-west transportation artery in the heart of Genoa, collapsed on Aug. 14, 2018, killing 43 people, there was little reason to think that its replacement would be in the final phases of construction less than two years later.	Genoa’s New Bridge Nears Completion, Turning Tragedy Into Hope	4	1	S	10	6	4	17	0	6	0	3	0	2	2	5
837	3826	The Upshot	The Upshot	Interactive Feature	Coronavirus Deaths by U.S. State and Country Over Time: Daily Tracker	Compare the number of deaths and the rate of increase over time in the places the virus has hit hardest so far.	['Coronavirus (2019-nCoV)', 'States (US)', 'Epidemics', 'Deaths (Fatalities)', 'United States', 'Spain', 'France', 'Italy', 'China', 'South Korea', 'Germany']	0	2020-03-21 14:21:03+00:00	1761	nyt://interactive/ac617f90-cbd1-5f17-abe4-db5bb065dce3	NaN	NaN	Compare the number of deaths and the rate of increase over time in the places the virus has hit hardest so far.	Coronavirus Deaths by U.S. State and Country Over Time: Daily Tracker	3	1	L	7	0	3	13	0	8	0	0	0	8	0	0
838	1564	National	U.S.	News	Pets Are Just ‘Property,’ So Owners Can’t Do Much When Vets Harm Them	Doctors who harm their patients face costly lawsuits and other serious consequences. There is much less accountability for veterinarians, as devastated pet owners in Oregon learned.	['Veterinary Medicine', 'Animal Abuse, Rights and Welfare', 'Pets', 'Dogs', 'Cats', 'California', 'Oregon', 'Koller, Daniel', 'Regulation and Deregulation of Industry', 'Suits and Litigation (Civil)']	1846	2020-02-04 10:00:27+00:00	293	nyt://article/a55a55bd-8221-508e-a704-f5da1a6c9b3b	A	12	BEAVERTON, Ore. — After his dog Bleu sustained a leg injury over the summer, Andres Figueroa brought the 7-month-old dachshund in for a checkup at a sleek suburban clinic outside Portland, Ore., that was decorated with cutouts of cheerful pets.	Pets Are Just ‘Property,’ So Owners Can’t Do Much When Vets Harm Them	2	1	M	10	7	6	13	3	1	0	1	3	2	2	2

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